Combined circulating tumor antigen model demonstrates additional prognostic value in first-line treatment of non-small cell lung cancer.

Authors

Charles W Tran, Wei Zou, Zoe J Assaf, Anika Mang, Francesco Ranucci, Jason H Cheng, Ivette Estay, Vy Ma, Minu K Srivastava, David S Shames, Stefan Holdenrieder, Martin Reck, Vinzent Rolny, Katja Schulze, Birgit Wehnl, Namrata S Patil

Published in

Journal for immunotherapy of cancer. Volume 14. Issue 7. Jul 30, 2026. Epub Jul 30, 2026.

Abstract

Circulating tumor antigens (ctA; tumor markers) are blood-based proteins that can offer prognostic value in non-small cell lung cancer (NSCLC) and may serve as potential early surrogates for survival. Given the significantly reduced testing time and cost of ctA compared with circulating tumor DNA (ctDNA), we further explored the utility of ctA using samples collected from over 2,300 patients participating in five clinical trials (IMpower130, 131, 132, 150, and 110).
We analyzed a panel of six ctA (CA125 (cancer antigen 125), CEA (carcinoembryonic antigen), Cyfra21-1 (cytokeratin 19 fragment 21-1; CYFRA), NSE (neuron-specific enolase), SCC (squamous cell carcinoma antigen), and ProGRP (progastrin-releasing peptide)) and CRP (C-reactive protein) from the serum of patients with metastatic NSCLC in these trials, which investigated combinations of atezolizumab (anti-programmed death-ligand 1)±bevacizumab±chemotherapy. Previous work showed that an optimized cut-off using two ctA or a machine learning (ML) model of ctDNA features, both taken at 6 weeks, can stratify patients with stable disease (SD) for survival risk in IMpower150. Building on this approach, we applied an ML model combining ctA features at baseline and at 6 weeks, trained across a much larger aggregate dataset from multiple clinical studies.
Previous findings from ctA analysis of IMpower150 were confirmed and found to be applicable to several other trials analyzed in this study. We found that ML model predictions provided similar prognostic performance (c-index of 0.73 and 0.71 in squamous and non-squamous test datasets, respectively), with CYFRA being the top feature for both histologies.
While ctA demonstrated limited potential in differentiating treatment effects to inform early drug development, deriving an optimal prediction cut-off for 1-year overall survival (OS) showed that ctA model predictions could effectively stratify patients by radiographic response with 61% sensitivity and 78% specificity, adding significant prognostic value to radiographic imaging. Patients with partial response (PR), progressive disease (PD), or stable disease (SD) at either 6 weeks of treatment or best confirmed overall response could be separated into low-risk or high-risk groups for OS.

PMID:
42532630
Bibliographic data and abstract were imported from PubMed on 31 Jul 2026.

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